Data Scientist

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Current Employees: If you are currently employed at any of the Universities of Wisconsin, log in to Workday to apply through the internal application process. Job Category: Academic Staff Employment Type: Regular Job Profile: Data Scientist II Job Summary: The Data Science Institute (DSI) is a campus-wide research institute that is central to the university's strategic priority to grow its research enterprise and expand its global impact. Our team of data scientists, software engineers, and AI engineers works shoulder-to-shoulder with faculty, students, and industry partners on problems across nearly every domain on campus. A typical week at DSI might include scoping a new collaboration with a principal investigator, pair-programming on a deep learning pipeline with another member of DSI's technical staff, and reviewing a statistical analysis for a health sciences team. The same week could bring teaching a workshop and mentoring a graduate student. The work is varied and the collaborators change, but the throughline remains constant: we bring rigorous, reproducible data science to researchers who need it and help the campus build lasting capacity along the way. DSI is also a core partner in the Wisconsin Research, Innovation and Scholarly Excellence (RISE) initiative, which is bringing over 150 new faculty to campus over three years and more than doubling investment in AI, sustainability, and health. As RISE expands, so does the demand for data scientists who can meet those faculty where they are and help turn ambitious research questions into impactful results. Learn more about DSI at https://dsi.wisc.edu/ and RISE at https://rise.wisc.edu/ . Who we're looking for: We are hiring two to three Data Scientists to join the team, based on-site in Madison. We are looking for candidates who will thrive in a research environment: people who are technically strong, intellectually curious, and energized by the chance to apply their skills across many different domains. The strongest candidates will bring deep expertise in at least one area of data science. Areas where we are especially looking to add depth include: Machine learning and deep learning, including computer vision, large language model (LLM) applications, retrieval-augmented generation (RAG), and agentic workflows, along with model training and production deployment. Statistical modeling and uncertainty quantification, including causal inference, Bayesian methods, meta-analysis, and study design. Data engineering for research-scale data: ingestion pipelines, distributed storage, and end-to-end handling of large or complex datasets that downstream modeling work depends on. This list is illustrative rather than exhaustive. Adjacent expertise you think we should know about is welcome in your application. Equally important is enough breadth and curiosity to contribute outside your core area when a project calls for it, and the judgment to know when to do so. Beyond technical depth, a few things matter to us. A collaborative instinct: our work is almost always in partnership with researchers, and the ability to listen, scope, and explain matters as much as the ability to model. A commitment to reproducible, well-engineered work: we want code that others can run, results others can trust, and methods others can learn from. A sense of stewardship for the data, the collaborators, and the junior colleagues and students who will use what we build. An openness to feedback: the ability to hear criticism as information about the work rather than the person, and to listen past what a collaborator literally asks for to the intent underneath. The strongest data scientists build things people actually use because they keep refining toward what the work actually needs. Key Job Responsibilities: Composes and assembles reproducible workflows and reports to clearly articulate patterns to researchers and/or administrators Prepares data sets for analysis including cleaning/quality assurance, transformations, restructuring, and integration of multiple data sources Documents approaches to address research questions and contributes to the establishment of reproducible research methodologies and analysis workflows Independently identifies and implements appropriate data science techniques to find data patterns and answer research questions chosen by the lead researcher including data visualization, statistical analysis, machine learning, and data mining Organizes and automates project steps for data preparation and analysis Engages in project intake, scopes new collaborations with researchers, and drafts statements of work for incoming engagements. Contributes to DSI's programmatic activities, including workshops, training, graduate student mentorship, and the institute's internal life. Department: Data Science Institute (DSI)

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